Video surveillance is widely used. For example, suspect search systems that identify, track and/or monitor an individual use video surveillance or video monitoring. Video Content Analysis (VCA) or video analytics are known and used, e.g., for automatic analysis of a video stream to detect or identify points of interest. Video analytics is becoming more prevalent in a wide range of domains such as security, entertainment, healthcare and surveillance.
However, systems that depend on input from cameras suffer from a number of drawbacks. Known systems use search algorithms or methods that may work well when provided with input from a single camera's field of view (FOV), but are unable to process multiple FOV's input. Other methods do process multiple FOV's, but assume clear overlaps between the FOV's, which, for most real-world scenarios, is not the case. Other known systems and methods are based on tracking, which is prone to fail in densely populated areas. Yet other systems and methods may fail when input images are acquired in varying conditions, e.g., a change in lighting, indoor/outdoor, angles, different cameras' settings, etc.